class_docstr: The holistic landmarks result from HolisticLandmarker, where each vector element represents a single holistic detected in the image.
  
  Attributes:
    face_landmarks: Detected face landmarks in normalized image coordinates.
    pose_landmarks: Detected pose landmarks in normalized image coordinates.
    pose_world_landmarks: Detected pose world landmarks in image coordinates.
    left_hand_landmarks: Detected left hand landmarks in normalized image
      coordinates.
    left_hand_world_landmarks: Detected left hand landmarks in image
      coordinates.
    right_hand_landmarks: Detected right hand landmarks in normalized image
      coordinates.
    right_hand_world_landmarks: Detected right hand landmarks in image
      coordinates.
    face_blendshapes: Optional face blendshapes.
    segmentation_mask: Optional segmentation mask for pose.
docstr: Creates a `HolisticLandmarkerResult` object from the given protobuf object.
docstr_tok: ["Creates","a","`","HolisticLandmarkerResult","`","object","from","the","given","protobuf","object","."]
code: def create_from_pb2(
      cls, pb2_obj: _HolisticResultProto
  ) -> "HolisticLandmarkerResult":
      """Creates a `HolisticLandmarkerResult` object from the given protobuf object."""
      face_blendshapes = None
      if hasattr(pb2_obj, "face_blendshapes"):
          face_blendshapes = [
              category_module.Category(
                  score=classification.score,
                  index=classification.index,
                  category_name=classification.label,
                  display_name=classification.display_name,
              )
              for classification in pb2_obj.face_blendshapes.classification
          ]
  
      return HolisticLandmarkerResult(
          face_landmarks=[
              landmark_module.NormalizedLandmark.create_from_pb2(
                  landmark
              )
              for landmark in pb2_obj.face_landmarks.landmark
          ],
          pose_landmarks=[
              landmark_module.NormalizedLandmark.create_from_pb2(
                  landmark
              )
              for landmark in pb2_obj.pose_landmarks.landmark
          ],
          pose_world_landmarks=[
              landmark_module.Landmark.create_from_pb2(landmark)
              for landmark in pb2_obj.pose_world_landmarks.landmark
          ],
          left_hand_landmarks=[
              landmark_module.NormalizedLandmark.create_from_pb2(
                  landmark
              )
              for landmark in pb2_obj.left_hand_landmarks.landmark
          ],
          left_hand_world_landmarks=[],
          right_hand_landmarks=[
              landmark_module.NormalizedLandmark.create_from_pb2(
                  landmark
              )
              for landmark in pb2_obj.right_hand_landmarks.landmark
          ],
          right_hand_world_landmarks=[],
          face_blendshapes=face_blendshapes,
          segmentation_mask=None,
      )
  
imports: import dataclasses
  from typing import Callable, List, Mapping, Optional
  from mediapipe.framework.formats import classification_pb2
  from mediapipe.framework.formats import landmark_pb2
  from mediapipe.python import packet_creator
  from mediapipe.python import packet_getter
  from mediapipe.python._framework_bindings import image
  from mediapipe.python._framework_bindings import packet
  from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_landmarker_graph_options_pb2
  from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_result_pb2
  from mediapipe.tasks.python.components.containers import category
  from mediapipe.tasks.python.components.containers import landmark
  from mediapipe.tasks.python.core import base_options
  from mediapipe.tasks.python.core import task_info
  from mediapipe.tasks.python.core.optional_dependencies import doc_controls
  from mediapipe.tasks.python.vision.core import base_vision_task_api
  from mediapipe.tasks.python.vision.core import image_processing_options
  from mediapipe.tasks.python.vision.core import vision_task_running_mode
label_desc: Use image node_id 1 for calling the HolisticLandmarkerResult obj's underlying member method code with example usage: obj.create_from_pb2(cls, pb2_obj) and returns: HolisticLandmarkerResult